Automated 3D Segmentation Using Deformable Models and Fuzzy Affinity
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Segmentation using deformable models with affinity-based localization
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Image Segmentation Based on the Integration of Pixel Affinity and Deformable Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A streaming narrow-band algorithm: interactive computation and visualization of level sets
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Image Processing
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We present a semi-automatic 3D segmentation method for brain structures from Magnetic Resonance Imaging (MRI). There are three main contributions. First, our method combines boundary-based and region-based approaches but differs from previous hybrid methods in that we perform them in two separate phases. This allows for more efficient segmentation. Second, a probability map is generated and used throughout the segmentation to account for the brain structures with low-intensity contrast to the background. Third, we develop a set of tools for manual adjustment after the segmentation. This is particularly important in clinical research because the reliability of the results can be ensured. The experimental results and validations on different data sets are shown.